Electronic health records-based algorithms to screen for U.S. Centers for Disease Control and Prevention tier 1 genetic diseases: a scoping review

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 32; no. 10; pp. 1629 - 1637
• Main Authors: Harris, William R, Hernandez, Marianna S, Ngo, Khanh N H, Fladger, Anne, Brunette, Charles A, Hamarneh, Sulaiman R, Knowles, Joshua W, Lebo, Matthew S, Vassy, Jason L
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.10.2025
• Subjects: Algorithms; Centers for Disease Control and Prevention, U.S; Colorectal Neoplasms, Hereditary Nonpolyposis - diagnosis; Electronic Health Records; Genetic Testing - methods; Hereditary Breast and Ovarian Cancer Syndrome - diagnosis; Humans; Hyperlipoproteinemia Type II - diagnosis; Hyperlipoproteinemia Type II - genetics; United States; United States; hereditary breast and ovarian cancer syndrome; electronic health records; machine learning; familial hypercholesterolemia; genomic screening; Lynch syndrome
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1093/jamia/ocaf140

Abstract Objective Missed diagnosis of genetic conditions is a persistent challenge in clinical care, particularly for familial hypercholesterolemia (FH), hereditary breast and ovarian cancer (HBOC), and Lynch syndrome—conditions designated by the U.S. Centers for Disease Control and Prevention (CDC) as Tier 1 genomic applications. This scoping review summarizes evidence on the use of electronic health record (EHR)-based algorithms to identify individuals with these conditions. Materials and Methods We conducted a scoping review using the JBI Manual for Evidence Synthesis and reported results according to PRISMA-ScR guidelines. We searched Ovid MEDLINE, Embase, and Web of Science through October 2024 for studies evaluating EHR-based algorithms to identify individuals with FH, HBOC, or Lynch syndrome. Eligible studies addressed (1) performance of algorithms in detecting clinically or genetically confirmed cases or (2) outcomes from the implementation of algorithms in unselected populations with follow-up to identify new diagnoses. Results Of 598 articles screened, 22 met inclusion criteria. Most studies (20/22) focused on FH. Fourteen FH studies assessed algorithm performance, and 7 reported prospective implementation. FH algorithm performance varied widely (AUROC range 0.78-0.95), with machine learning models outperforming rule-based approaches. Implementation studies reported positive predictive values ranging from 11% to 67%. Only two studies addressed HBOC or Lynch syndrome, both using rules-based algorithms with limited sensitivity. Discussion Machine learning models consistently outperform rules-based algorithms relying on clinical criteria, but limited evidence exists for HBOC and Lynch syndrome. Conclusions Early identification of CDC Tier 1 genetic conditions through EHR-based screening algorithms holds promise but will require both technical and implementation advances to realize improved patient care and outcomes.